Find Academic Supervisors

• Biodiversity Conservation
• Invasive Species Management
• Endangered Species Recovery
• +4 more

Alison Copeland is a Research Postgraduate (PhD) in the Department of Biosciences at Durham University. Her research focuses on Bermuda’s vegetation communities, particularly invasive plant species and their interactions with indigenous flora. Previously, she served as Biodiversity Officer for Bermuda’s Government, leading projects like the IUCN Red Listing of endemic plants and the Governor Laffan’s Fern recovery initiative. She holds an MSc in Geography from Memorial University of Newfoundland (MUN), where her thesis involved seabed habitat mapping in Newman Sound fjord using multibeam sonar and field sampling. Her expertise spans marine and terrestrial conservation, with a focus on endangered species recovery, habitat management, and translating science into policy. Key affiliations include MUN’s Marine Habitat Mapping Group and the Royal Botanic Gardens Kew. Awards include the Durham Doctoral Studentship (Faculty of Science) and a Bermuda Public Service Education Award. Her recent work highlights invasive species dynamics in island ecosystems, biodiversity assessments, and policy frameworks for conservation. She has contributed to over 15 peer-reviewed articles, emphasizing habitat mapping methodologies, invasive species management, and endangered species recovery strategies. Awards: $3000 Garden Club of Bermuda Grant (PhD support) Fellow of MUN’s School of Graduate Studies Grants: Durham Doctoral Studentship (2022–present) Her PhD project investigates the ecological origins and interactions of invasive plants in Bermuda, bridging field research with policy recommendations to enhance biodiversity protection.

Kasper WelbersView profile

Associate Professor

• Computational Communication Science
• Political Communication
• Journalism Studies
• +7 more

Kasper Welbers is an Associate Professor at the Department of Communication Science, Faculty of Social Sciences, Vrije Universiteit Amsterdam. He holds additional appointments at the Network Institute and the Communication Choices, Content and Consequences (CCCC) research group. He is co-Director of The Societal Analytics Lab, Vice Chair of the Computational Methods Division at the International Communication Association (ICA), and a scientific representative of the OPINION COST Action network. His research focuses on computational communication science, journalism, and political communication, particularly exploring how news spreads and the role of gatekeepers in media systems. He develops open-source tools for research and advocates for reproducible computational methods. Education and Academic Background: While formal education details are not explicitly provided, his academic trajectory is evident through his faculty role and publications. Research Interests: Welbers combines methodological innovations with substantive research on news diffusion, media gatekeeping, and computational text analysis. His work addresses topics like dark platforms' agenda-setting roles, automated content analysis techniques, and the impact of platform policies on data donation studies. He emphasizes ethical research practices and open-source infrastructure development. Grants and Awards: He received the Faculty of Social Sciences Dissertation Award (2017) and Teacher Talent Award (2019), highlighting both research and pedagogical excellence. Labs and Collaborations: His work is anchored in The Societal Analytics Lab, fostering interdisciplinary research on societal challenges through computational methods. Collaborations span global networks like the OPINION COST Action, addressing cross-cultural media dynamics. Teaching: He teaches courses such as Computational Analysis of Digital Communication and contributes to curriculum development in AI ethics and data science for societal issues.

Brady NelsonView profile

Associate Professor

• Affective Neuroscience
• Psychophysiology
• Anxiety Disorders
• +3 more

Dr. Brady Nelson is an Associate Professor in the Department of Psychology at Stony Brook University, specializing in clinical affective neuroscience. He holds a Ph.D. from the University of Illinois at Chicago (2013). His primary research examines the neural mechanisms underlying anxiety disorders and depression, focusing on reward sensitivity and unpredictable threat reactivity in developmental populations. Key methodologies include EEG, fMRI, and psychophysiological measures. Current projects include the iPANDA longitudinal study tracking reward sensitivity and depressive symptoms in adolescent girls. Dr. Nelson has secured $9.1 million in federal research funding since 2018, including NIH grants for studies on psychopathology biomarkers and pandemic mental health impacts. He directs the Laboratory for Clinical Affective Neuroscience and has authored over 80 peer-reviewed articles. His work emphasizes translational neuroscience, bridging basic research to clinical applications for mental health prevention and intervention. Active grants include investigations into maternal parenting interventions and the neurobiological foundations of internalizing disorders. Dr. Nelson advises on graduate studies but will not review applications for the 2025-2026 academic year.

Paul NuyujukianView profile

Assistant Professor

• Motor Systems Neuroscience
• Neuroengineering
• Brain-Machine Interfaces
• +4 more

Paul Nuyujukian serves as an Assistant Professor of Bioengineering and Neurosurgery, with courtesy appointment in Electrical Engineering at Stanford University. He is a Faculty Scholar of the Wu Tsai Neurosciences Institute, directing the Brain Interfacing Laboratory where his team develops neural interface technologies for clinical applications in stroke and epilepsy. Education: MD, Stanford University (2014) PhD in Bioengineering, Stanford University (2012) BS, UCLA (2006) Dr. Nuyujukian's research integrates motor systems neuroscience with neuroengineering to decode brain activity during movement and recovery from injury. His laboratory pioneers brain-machine interface (BMI) platforms that translate neural signals into communication and control systems, with particular emphasis on intracranial EEG recording and real-time neural decoding. Current work focuses on developing clinically viable BMI solutions for neurological conditions through both preclinical models and human trials, advancing our understanding of neural population dynamics in health and disease. Recent publications reveal strong trends in intracranial EEG acquisition systems, seizure detection algorithms using information theory, and closed-loop BMI applications for ambulatory neuroscience. His work bridges fundamental neuroscience with clinical translation, particularly in epilepsy monitoring, chronic pain management, and neural prosthetics for paralysis. A notable emphasis exists on creating scalable, minimally invasive recording platforms that reduce clinical burden while maintaining high-fidelity neural data. Scientific Awards: No specific awards listed in provided materials As director of the Brain Interfacing Laboratory, Dr. Nuyujukian mentors students and collaborators in neural engineering research while securing grant funding for BMI development. His group maintains active collaborations with Stanford's Department of Neurosurgery and Neurology for clinical translation, with current projects including real-time decision-state decoding and personalized network mapping for pain management. The laboratory operates advanced facilities for both animal and human neural recording, emphasizing seamless integration of engineering innovation with clinical neuroscience. The Brain Interfacing Laboratory comprises multidisciplinary scientists and engineers developing next-generation neural interfaces. Current initiatives include the LiCoRICE platform for ambulatory neuroscience, seizure detection systems using compression-enabled entropy estimation, and ketamine's effects on hippocampal connectivity. The team actively participates in clinical trials for BMI applications in stroke rehabilitation and epilepsy, with strong partnerships across Stanford's medical and engineering schools to accelerate technology translation.

Rosario B. Jaime-LaraView profile

Assistant Professor

• Nutrition
• Neurophysiology
• Obesity Research
• +5 more

Rosario B. Jaime-Lara is an Assistant Professor at the University of California, Los Angeles (UCLA) School of Nursing. She holds advanced degrees including a PhD in Nursing from the University of Pennsylvania, MSN from Columbia University, and dual BS degrees in Nursing and Biological Sciences from University of Pennsylvania and UC Davis. Her research focuses on neurophysiological mechanisms of eating behavior, nutritional disparities in Mexican-American communities, and chemosensory science. Key themes include obesity research, sensory neuroscience, and translational clinical studies. She employs rodent models and clinical research methodologies to explore taste/smell physiology and metabolic health. Jaime-Lara has received over 17 honors including the 2022 Hommer Memorial Award and multiple diversity fellowships. Her work spans interdisciplinary collaborations in genomics, microbiome research, and health IT interventions for chronic disease management. Recent publications emphasize olfactory dysfunction in coronaviruses, fat taste mediators, and metabolic profiling. Her research bridges basic science and clinical practice, with particular attention to underserved populations.

• Immunology
• Systems Biology
• Genomics
• +4 more

Dr. Masahiro Ono is a Reader in Immunology at Imperial College London's Department of Life Sciences, within the Faculty of Natural Sciences. He leads research on T-cell regulation, focusing on autoimmunity, infections, and cancer. His lab pioneered the Tocky system, using Fluorescent Timer proteins to study T-cell activity dynamics in vivo. Dr. Ono holds affiliations with the CRUK Convergence Science Centre, Infection and Immunity, and Integrative Systems Biology. His academic journey includes an MD from Kyoto University (1993-1999) and a PhD in regulatory T cells (2002-2006). He was awarded a HFSP Fellowship (2009) and BBSRC David Phillips Fellowship (2012), establishing his UCL lab before joining Imperial in 2015. Research Interests : Dr. Ono's work bridges immunology, genomics, and systems biology. His lab explores T-cell activation mechanisms, tumor immunology, and bioinformatics tools for single-cell analysis. Key innovations include the Tocky system for real-time cell kinetics tracking and integrative approaches like GatingTree for cytometry data analysis. Awards : HFSP Fellowship (2009) BBSRC David Phillips Fellowship (2012) Grants & Advising : Dr. Ono's grants have supported projects on viral latency, tumor microenvironment modulation, and immune checkpoint therapies. His lab actively collaborates on translational research, though specific grant details are not listed here. Labs & Teams : His lab at Imperial focuses on Tocky-based technologies and interdisciplinary convergence science through the CRUK Centre. Collaborations span virology, oncology, and bioengineering to address unmet clinical needs in immunotherapy.

Haiyan HuangView profile

Professor

• Computational Biology
• Applied Statistics
• High Dimensional Data Analysis
• +3 more

Haiyan Huang is a Professor and Chair of the Department of Statistics at the University of California, Berkeley. She is affiliated with the Center for Computational Biology and the Graduate Group in Biostatistics. Her research focuses on computational biology, applied statistics, and high-dimensional genomic data analysis, with an emphasis on network modeling and translational bioinformatics. Education details are not explicitly listed, but her academic background includes a PhD in Statistics. She has advised numerous graduate students and postdocs, contributing to impactful research in statistical methods for genomics and bioinformatics. Her work spans statistical methodology development, including sparse canonical correlation analysis, biclustering, and single-cell RNA sequencing analysis. Notable projects include predicting tumor heterogeneity, designing random heteropolymers, and linking protein expression to cell motility. Publications highlight her contributions to gene network inference, reproducibility in high-throughput experiments, and integrating multi-platform genomic data. Awards include recognition by Faculty of 1000 Biology for her 2010 PNAS work. Huang teaches advanced courses in computational biology, statistical theory, and consulting. Her lab collaborates with experts in bioengineering, chemistry, and pediatrics, fostering interdisciplinary research. Current projects include precision medicine, deep learning applications, and probabilistic modeling of biological systems. Her team uses variational inference, hidden Markov models, and machine learning to address challenges in single-cell analysis and pharmacogenomics. The lab’s work has been published in top journals like Nature, PNAS, and Bioinformatics, reflecting her leadership in computational genomics.

• Biomedical Engineering
• Tissue Engineering
• 3D Printing
• +5 more

Neil Lin is an Assistant Professor in the Department of Mechanical and Aerospace Engineering at the University of California, Los Angeles (UCLA), with a joint appointment in Bioengineering. His research focuses on developing 3D-printed tissues that replicate the structure, mechanics, and functionality of human organs, with applications in drug screening and regenerative medicine. Lin leads the Lin Lab - Living Soft Material Engineering , advancing soft and living material engineering through interdisciplinary approaches. Lin holds a Ph.D. and M.S. in Engineering from Cornell University (2016 and 2013) and a B.S. in Engineering from National Tsing Hua University, Taiwan (2008). His work spans biomaterials mechanics, quantitative imaging, and AI-driven biological analysis. Research interests include the structure and dynamics of soft biomaterials, image-based force measurements, and quantitative imaging techniques for material characterization. Recent work explores cell morphology regulation, AI applications in microscopy, and mechanical heterogeneity in live tissues. Lin’s publications highlight advancements in cell trapping, AI-based image translation, and prostate cancer metabolism modeling. He has received prestigious awards, including the Young Investigator Award (2022), Hellman Fellowship, and NIH Maximizing Investigators' Research Award (2022). His lab integrates engineering and biology to address challenges in tissue engineering, with ongoing projects on 3D kidney models, drug response assays, and AI-driven phenotyping of senescent cells.

Skyler WangView profile

Assistant Professor

• AI
• Technology
• Human-Computer Interaction
• +7 more

Skyler Wang is an Assistant Professor of Sociology at McGill University, specializing in AI, technology, and human-computer interaction. He holds a Ph.D. from UC Berkeley and previously served as a Sociologist at Meta’s FAIR lab. His research critically examines sociotechnical systems' epistemic cultures and social impacts, focusing on AI-driven human-machine interactions in health, relational contexts, and digital platforms. His research interests include AI ethics, platform societies, digital intimacy, and multilingual systems. Notable works include the book project Sharing Bodies in the Sharing Economy , exploring Couchsurfing’s sociosexual dynamics, and applied AI projects like No Language Left Behind (doubling machine translation languages) and SeamlessM4T (awarded TIME’s 2023 Best Inventions). Teaching focuses on Technology & Society, Artificial Intelligence & Society, and Digital Intimacy. Advising roles include Major/Minor and Honours Sociology students. Active in interdisciplinary collaborations through McGill’s Quebec Inter-University Centre for Social Statistics and global AI ethics initiatives. Education: Ph.D. Sociology, UC Berkeley (2023) Key Affiliations: Meta FAIR Lab (prior), McGill Department of Sociology Publications in Nature , CSCW , Big Data & Society , and media features in WIRED, CNN, and NPR

David J ReinkensmeyerView profile

Professor

• Neuroengineering
• Rehabilitation Robotics
• Motor Control
• +3 more

David J. Reinkensmeyer is a Professor at the University of California, Irvine (UCI), holding appointments in the Department of Mechanical & Aerospace Engineering (The Henry Samueli School of Engineering), Anatomy & Neurobiology (School of Medicine), and Biomedical Engineering. His research focuses on neurorehabilitation engineering, developing robotic and sensor-based technologies to enhance motor recovery after neurological injuries such as stroke and spinal cord injury. He leads interdisciplinary efforts in robotic therapy, sensor design for movement assessment, and computational models of motor learning. Key areas include: Design of robotic devices for upper/lower extremity rehabilitation Development of wearable sensors to monitor home exercise programs Studying motor adaptation and proprioception in clinical populations Optimizing neurorehabilitation interventions through computational modeling His work integrates biomechanics, machine learning, and clinical neuroscience to create practical solutions for disabling movement disorders. Recent projects explore scalable mRehab systems, data-driven diagnostics, and real-time feedback technologies. Funding sources include NIH grants (e.g., R01 HD062744) and industry collaborations. Over 200 peer-reviewed publications and numerous patents reflect his impact in translating engineering innovations into clinical practice.

Seth FreyView profile

Associate Professor

• Computational Social Science
• Governance Institutions
• Online Communities
• +3 more

Seth Frey is an Associate Professor in the Department of Communication at the University of California, Davis. His research focuses on computational social science, exploring governance institutions and complex human decision-making through computational methods, large datasets, and web-based experiments. He examines online communities as models of governance, with expertise in computational approaches to institutional design and strategic behavior analysis. Education: Ph.D. in Cognitive Science and Informatics, Indiana University, 2013 B.A. in Cognitive Science, UC Berkeley, 2004 Research Interests: Frey's work integrates data science, lab experiments, and computational modeling to study human organizations and communication. Key areas include governance technology, cognitive mechanisms of social outcomes, and institutional evolution. His projects span online games, sports, and open-source software communities, emphasizing topics like collective action, self-governance, and cooperative behavior. Publications & Funding: His work appears in journals like PNAS and Nature Scientific Reports, and has been funded by NSF, NASA, and the Ford Foundation. He contributes to the Ostrom Workshop at Indiana University and directs the Computational Communication Lab at UC Davis. Teaching: Frey teaches courses on data visualization, simulation methods, and online data analysis in the social sciences.

• Artificial Intelligence Ethics
• Healthcare Machine Learning
• Algorithmic Bias
• +3 more

Dr. Melissa McCradden is a THRF Clinical Research Fellow at the Australian Institute for Machine Learning (University of Adelaide), Deputy Research Director at the Women's and Children's Health Network, and an Adjunct Scientist at the SickKids Research Institute (Toronto). Her work focuses on ethical frameworks for integrating AI into healthcare, addressing algorithmic bias, and advancing clinical evaluation standards. She holds a PhD in Neuroscience and a Master’s in Bioethics, and has pioneered initiatives like the STANDING Together consensus for AI ethics. Education: PhD in Neuroscience (McMaster University, 202?), Master of Health Sciences in Bioethics (University of Toronto, 20?), inaugural Postdoctoral Fellow in AI Ethics at SickKids/Vector Institute. Research Interests: Ethical AI integration in healthcare, clinical trial methodologies (CONSORT-AI, SPIRIT-AI), bias mitigation in medical datasets, and pediatric AI ethics. Her projects include CANAIRI (AI trials collaboration) and promoting AI literacy among patients. Awards: Listed among the 100 Brilliant Women in AI Ethics. Grants: THRF Fellowship, WHO Clinical Evaluation Task Force involvement. Supervision: Co-supervisor for PhD candidate Lana Tikhomirov (2024). Grants include Centre for Augmented Reasoning funding and Canadian Institutes of Health Research. Labs/Teams: Deputy Director at Australian Institute for Machine Learning, member of WHO’s Clinical Evaluation Task Force for Healthcare AI, and co-chair of DECIDE-AI guidelines for first-in-human trials.

• Biomedical Instrumentation
• Medical Technology
• Clinical Engineering
• +3 more

Jan Olav Høgetveit is an Associate Professor in the Department of Physics at the University of Oslo (UiO), within the Faculty of Mathematics and Natural Sciences. He also serves as Head of Research & Development in the Department of Biomedical and Clinical Engineering at Rikshospitalet, Norway’s national hospital. His work bridges physics and clinical practice, focusing on medical instrumentation. Education: Bachelor of Electronic Engineering (Technical Cybernetics), Oslo University College, 1993 Master of Electronics, University of Oslo (Department of Physics), 1997 Ph.D. in Physics (Technology Applications for Medical Devices), University of Oslo, 2008 Research Interests: Høgetveit specializes in biomedical instrumentation and clinical engineering, particularly in surgical technology and wireless communication impacts on medical devices. His work addresses challenges like real-time physiological monitoring during heart-lung machine use, non-invasive blood glucose detection, and bioimpedance-based viability assessment of organs. He emphasizes interdisciplinary collaboration between engineering and medicine to enhance patient safety and surgical outcomes. Scientific Contributions: His research trends span bioimpedance applications in ischemia/reperfusion injury, machine learning for surgical decision support, and electrosurgery safety. He has explored ventilator optimization during pandemics and implant-related thermal risks. Contributions highlight both hardware development (e.g., optically isolated current sources) and software innovations (e.g., neural networks for viability prediction). Awards: No scientific awards explicitly mentioned in the text. Advising & Grants: Høgetveit received a 1998–2001 research council scholarship. As Head of R&D since 2001, he oversees translational projects. No formal advisees/students listed, though he collaborates extensively with teams on device development and clinical trials. Labs & Teams: Affiliated with UiO’s Department of Physics and Rikshospitalet’s Biomedical and Clinical Engineering department. Active in the Electronics research group at UiO. Engages with multidisciplinary teams addressing surgical instrumentation and physiological monitoring challenges.

• Computational Linguistics
• Text Summarization
• Information Extraction
• +6 more

Horacio Saggion is the Chair in Computer Science and Artificial Intelligence at the Department of Information and Communication Technologies, Universitat Pompeu Fabra. He leads the TALN Group and the Large Scale Text Understanding Systems Lab. His research focuses on Computational Linguistics, with specialties in Text Summarization, Information Extraction, and Semantic Analysis. He coordinates the Horizon Europe iDEM project on inclusive democratic spaces and previously led the SignON project for Sign Language Translation. Key technologies include the SUMMA Summarization system and the Dr Inventor Text Mining Library. Education: PhD, MSc, and Licenciatura in Computer Science. Research Interests: Text simplification for accessibility, sign language translation, misinformation detection, and ethical AI applications. His work bridges natural language processing with societal needs such as clear communication in public administration. Grants & Projects: Coordinator of iDEM (Horizon Europe), PI of SignON, Simplext, and Able to Include. Involved in BEA shared tasks and CLEF labs. Active in organizing workshops like TSAR at EMNLP. Labs & Teams: Head of TALN Group and Text Understanding Lab. Collaborations include Universitat Pompeu Fabra's interdisciplinary initiatives and industry partnerships for technology commercialization.

Eduardo GildinView profile

Professor

• Reservoir Modeling and Simulation
• Control and Optimization of Dynamical Systems
• Model Reduction Techniques
• +5 more

Eduardo Gildin is a Professor of Petroleum Engineering and Associate Department Head for Graduate Studies at Texas A&M University's College of Engineering. He holds the L.F. Peterson '36 Professorship and directs the university's graduate studies in petroleum engineering. His research focuses on reservoir modeling, control optimization, model reduction techniques, and CO2 sequestration. Gildin has pioneered data-driven approaches for reservoir simulation, integrating machine learning and physics-based models to enhance efficiency and accuracy. Education: Ph.D. in Aerospace Engineering, University of Texas at Austin (2006) M.S. in Mechanical Engineering, University of São Paulo, Brazil (1998) B.S. in Mechanical Engineering, Faculdade de Engenharia Industrial, Brazil (1995) Research Interests: Model reduction of large-scale dynamical systems Control and optimization of reservoir operations CO2 storage and geological carbon sequestration Machine learning applications in reservoir engineering and drilling automation Geomechanics and compaction damage evaluation Key Awards: 2020: William O. and Montine P. Head Memorial Research Award 2017-2018: Dean of Engineering Excellence Award 2013-2019: Energi Simulation Chair in Robust Reduced Complexity Modeling 2021: Distinguished Membership in Society of Petroleum Engineers Grants and Advising: Gildin has secured major funding for projects on reservoir simulation, drilling automation, and CO2 storage. He advises graduate students on topics such as surrogate modeling and reinforcement learning applications in petroleum systems. His lab collaborates with industry partners to translate research into practical tools for reservoir management and subsurface operations. Labs and Teams: He leads the Reservoir Simulation and Control Lab, focusing on advanced computational methods for reservoir optimization. His team develops open-source drilling models and collaborates globally on projects like the DREAMS (Drilling and Extraction Automated System) initiative.